Coupling element for semiconductor neural network device

ABSTRACT

A neural network device includes internal data input lines, internal data output lines, coupling elements provided at the connections of the internal data input lines and the internal data output lines, word lines each for selecting one row of coupling elements. The coupling elements couple, with specific programmable coupling strengths, the associated internal data input lines to the associated internal data output lines. In a program mode, the internal data output lines serve as signal lines for transmitting the coupling strength information. Each of the coupling elements includes memories constituted of cross-coupled inverters for storing the coupling strength information, first switching transistors responsive to signal potentials on associated word lines for connecting the memories to associated internal data output lines, second switching elements responsive to signal potentials on associated internal data input lines for transmitting the storage information in the memories to the associated internal data output lines. Each of the internal data output lines has a pair of first and second internal data output lines.

CROSS REFERENCE

This application is related to the copending application Ser. No.07/605,717 under the name of the same inventor, K. Mashiko, and assignedto the same applicant, Mitsubishi Denki.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to semiconductor neuralnetworks, and more particularly, to coupling elements which couple withspecific coupling strengths (synapse loads) internal data input lines tointernal data output lines, and to improvements of a neural networkdriving method.

2. Description of Background Art

In recent years, a variety of circuits modeled on a neuron of a humanbeing has been contrived. Among such neuron models, there is one calleda Hopfield's model. This Hopfield's model will be briefly describedbelow.

In FIG. 1, there is shown a schematic structure of a unit modeled on aneuron. A unit i comprises an input portion A for receiving signals fromother units k, j, and the like, a converting portion B for convertingapplied input signals according to a certain rule, and an output portionC for outputting the conversion results. The input portion A has aweight (synapse load) W for each input unit which indicates a couplingstrength between the units. Therefore, an input signal Sk from the unitk is loaded with a weight Wik at the input portion A before transmittedto the converting portion B. This weight Wik can take any of positiveand negative values or 0.

The converting portion B make a total sum "net" of inputs S that havebeen loaded with the weights W (when generically termed, the weight isreferred to as "W" hereinafter) undergo a predetermined function f foroutput. Output Si from the unit i at the time t is given as: ##EQU1## Asthe function f, a threshold function shown in FIG. 2A or a sigmoidfunction shown in FIG. 2B is often used.

The threshold function shown in FIG. 2A is a unit step function havingcharacteristics that when the total sum "net (i)" of inputs becomeslarger than a threshold value θ, "1" is output, and when it does notreach the threshold value, "0" is output.

The sigmoid function shown in FIG. 2B is a non-linear monotonouslyincreasing function and given by the following expression:

    f=1/[1+exp(-net(i))].

The range of values of the sigmoid function is from 0 to 1. Therefore,as the total sum "net (i)" of inputs becomes smaller, the outputapproaches to "0", and as the total sum "net (i)" of inputs becomeslarger, the output approaches to "1". When the total sum "net (i)" ofinputs is "0", this sigmoid function outputs "0.5".

Another function obtained by adding a predetermined threshold value θ tothe above-mentioned sigmoid function, as given by the followingexpression, may be employed.

    f=1/[1+exp(-net(i)+θ)]

The unit model above is modeled on a vital cell which receives stimulifrom other neurons and fires when a total sum of the stimuli exceeds acertain value. The Hopfield's model provides an operational model to anetwork configured of a plurality of such neuron units.

In the expressions above, when one neuron is initialized, state of allthe remaining neuron units is determined in principal by applying theabove-mentioned two dynamic equations to each neuron unit and solvingthem simultaneously. When the number of units increases, however, it isalmost impossible to investigate and catch hold of state of one unitafter another, and to program weights and bias values such that anoptimal solution can be provided for a target problem. Therefore,Hopfield has introduced, in place of state of each unit, an energyfunction E as a quantity for representing entire characteristics of aneural net, which is defined as follows. ##EQU2## In the expressionabove, Ii is a self-bias value specific to the unit i. Hopfield hasdemonstrated that when the weight (synapse load) Wij has a symmetryshown as Wij=Wji, each unit changes its own state such that theabove-mentioned energy function E always takes minimum values (morecorrectly, local minima), and proposed that this model be applied toprogramming of the weight Wij. A model according to the energy functionE as described above is called a Hopfield's model. The expressions aboveare often restated for a discrete model as: ##EQU3##

In the expression above, n is a discrete time. Hopfield himself hasdemonstrated that the Hopfield's model above can work with good accuracyespecially when the function f indicating input/output characteristicshas a steep gradient (which is approximate to a unit step function inwhich most of the outputs take values close to either "0" or "1").

Neural networks have been configured according to this Hopfield's modelin VLSI (Very Large Scale Integration) technology. An example of such aneural network is disclosed in "Computer", March, 1988, pp. 41 to 49,published by IEEE (Institute of Electrical and Electronics Engineers),or in "A CMOS Associative Memory Chip Based on Neural Network", by H. P.Graf in "87 ISSCC, Digest of Technical Papers", 1987 February, pp. 304to 305, published by IEEE.

In FIG. 3, there is shown the entire schematic structure of aconventional integrated neural network circuit disclosed in thedocuments above. Referring to FIG. 3, the conventional integrated neuralnetwork circuit comprises a resistive matrix 100 having resistivecoupling elements with predetermined weights arranged in a matrix, andan amplifying circuit 101 for amplifying potentials on internal datainput lines included in the resistive matrix 100 and feeding back thoseamplified signals to the input portions of the respective resistivecoupling elements. The resistive matrix 100 comprises the internal datainput lines and internal data output lines arranged in a directionorthogonally intersecting the internal data input lines, as will bedescribed in detail later. Interconnections between the internal datainput lines and the internal data output lines made through theresistive coupling elements can be programmed by programming resistancevalues of the resistive coupling elements.

To select resistive coupling elements contained in the resistive matrix100, there are provided a row decoder 102 and a bit decoder 103. The rowdecoder 102 selects one row of coupling elements in the resistive matrix100. The bit decoder 103 selects one column of coupling elements in theresistive matrix 100.

To write coupling strength information in the resistive couplingelements selected by the row decoder 102 and the bit decoder 103, thereare provided an input/output data register 104 for temporarily latchingapplied data, a multiplexer 105 for connecting the input/output dataregister 104, according to write/read mode of the data, to either theinternal data input lines or the internal data output lines in theresistive matrix 100, an interface (I/O) 106 for connecting theinput/output data register 104 to the outside of the device. This neuralnetwork is integrated on a semiconductor chip 200.

The row decoder 102 and the bit decoder 103 select a single resistivecoupling element, to which information of coupling strength is writtenin through the input/output data register 104 and the multiplexer 105.Thus, states of the respective coupling elements contained in theresistive matrix 100 are determined, or interconnection states of theinternal data input lines and the internal data output lines can beprogrammed in this manner.

FIG. 4 shows more specifically an example of structure of the resistivematrix 100 shown in FIG. 3. Referring to FIG. 4, the resistive matrix100 comprises internal data input lines A1 to A4 and internal dataoutput lines B1 and B1, B2 and B2, B3 and B3, and B4 and B4. At theconnections between the internal data input lines A1 to A4 and theinternal data output lines B1 and B1 to B4 and B4, there are providedresistive coupling elements 1. Each coupling element 1 can take threestates; "open state", "excitatory state" and "inhibitory state". Thestate of each resistive coupling element 1 can be externally programmedaccording to an applied problem. Though in FIG. 4, those resistivecoupling elements 1 that are in the open state are not shown, all theconnections between the internal data input lines and the internal dataoutput lines are provided with the resistive coupling elements 1. Eachresistive coupling element 1 transmits, according to its own programmedstate, signal potential level on the corresponding internal data outputline Bi (Bi) onto the corresponding internal data input line Aj.

For the internal input lines A1 to A4, there are provided amplifyingcircuits C1 to C4 for amplifying signal potentials on the correspondinginternal data input lines and transmitting the amplified potentials tothe corresponding internal data output lines. Each of the amplifyingcircuits C1 to C4 has two inverting amplifiers 2a and 2b connected inseries. The inverting amplifier 2a inverts potential on the input lineAi and transmits the inverted potential onto the internal data outputline Bi. The inverting amplifier 2b transmits the signal potential onthe input line Ai onto the complementary internal data output line Bi.

Each of the resistive coupling elements 1 couples output of oneamplifier Ci to input of another amplifier Cj. A specific structure ofthe resistive coupling element 1 is shown in FIG. 5.

Referring to FIG. 5, the resistive coupling element 1 comprises currentlimiting resistive elements R+ and R-, random access memory cells 150and 151 for storing coupling strength information, switching elements S1and S2 responsive to an output signal of an amplifying circuit Ci to beturned on/off, and switching elements S3 and S4 responsive to theinformation stored in the random access memory cells 150 and 151 to beset in the on/off state. The resistive element R+ has one terminalconnected to a supply potential V_(DD). The resistive element R- has oneterminal connected to another supply potential (for example, groundpotential) V_(SS). The switching element S1 is controlled by output ofan inverting amplifier 2b for its on/off. The switching element S2 isturned on/off according to the information stored in the random accessmemory cell 150. The switching element S3 is set in the on/off stateaccording to the information stored in the random access memory cell151. The switching element S4 is controlled by output of anotherinverting amplifier 2a for its on/off.

In order to write information indicative of coupling strength into therandom access memory cells 150 and 151, word lines WLP and WLQ and a bitline BL are provided. The random access memory cell 150 is provided atthe crossing of the word line WLP and the bit line BL. The random accessmemory cell 151 is provided at the crossing of the word line WLQ and thebit line BL. The random access memory cell 150 stores informationindicative of "excitatory state" and the random access memory cell 151stores information indicative of the "inhibitory state". Thus, two wordlines are provided for a single coupling element. The word lines WLP andWLQ receive row select signals from the row decoder 102 shown in FIG. 3.The bit line BL is selected by the bit decoder 103 shown in FIG. 2 toreceive coupling strength information. The word lines WLP and WLQ areprovided in parallel with the internal data input line Ai, and the bitline BL is in parallel with the internal data output line Bi in theresistive matrix.

In the structure shown in FIG. 5, output of an amplifying circuit Cidoes not directly supply current to a corresponding internal data inputline, thereby reducing output load capacitance of the amplifying circuitCi. The coupling element 1 can selectively take three states, asdescribed above, according to the programmed states of the random accessmemory cells 150 and 151. That is, the "excitatory state" where theswitching element S2 is in on the state (active state), the "inhibitorystate" where the switching element S3 is in the on state (active state),and the "open (don't care) state" where both switching elements S2 andS3 are in the off state (non-active state).

When potential levels on output lines Bi and Bi of an amplifying circuitCi coincide with a programmed coupling state of a certain resistivecoupling element 1, current flows into a corresponding data input lineAi either from the supply potential V_(DD) or from the other supplypotential (ground potential) V_(SS). When the programmed coupling stateof the resistive coupling element 1 is "open, no current flows throughthe input line Ai irrespective of output state of the amplifying circuitCi.

When the above-mentioned circuit model is compared to a neuron model,the amplifying circuit Ci corresponds to a neuron body (the convertingportion B in FIG. 1). The signal lines A1 to A4, and B1 to B4 and B1 toB4 correspond to the data input portion A and the data output portion C(or dendrite and axon) shown in FIG. 1, respectively. The resistivecoupling element 1 corresponds to a synapse loading portion whichprovides weighting between neurons. Now, operation of the resistivematrix will be briefly described.

The model shown in FIG. 4 is often called a connectionists' model. Inthis model, each neuron unit (amplifying circuit Ci) simply performsthresholding of an input signal, or outputs a signal corresponding tomagnitude of the input signal compared with a predetermined thresholdvalue. Each resistive coupling element couples output of one amplifyingcircuit Ci to input of another amplifying circuit Cj. Therefore, stateof each amplifying circuit Ci is determined by states of all theremaining amplifying circuits Cj (i≠j). When a certain amplifyingcircuit Ci detects current on the corresponding input line Ai (i=1 to4), output of the amplifying circuit Ci at that time is given as:##EQU4## In the expression above, Vin (j) and Vout (j) represent inputand output voltages, respectively, of the amplifying circuit Cjconnected to an internal data input line Aj, Ij represents currentflowing through a single resistive coupling element, Wij representsconductance of a resistive coupling element which couples the amplifyingcircuit Ci connected to the internal data input line Ai to theamplifying circuit Cj connected to the internal data input line Aj. Theoutput voltage Vout of the amplifying circuit Ci is determined bytransfer characteristics of the amplifying circuit Ci itself. Thevoltage applied to the amplifying circuit Ci from the input line Ai isgiven by a total sum of currents flowing into the input line Ai. Thisinput voltage is adjusted such that the total current flowing in thisnetwork becomes 0. In such state, the total energy of the neural networkreaches local minima.

Each of the amplifying circuits Ci is constituted of, for example, aCMOS inverter. When the CMOS inverter has a high input impedance and anon-linear monotonously increasing threshold function as describedabove, the following relational expression can be obtained from theabove-described condition that the total current becomes 0. ##EQU5## Inthe expression above, Iij represents current flowing through theresistors of a resistive coupling element controlled by output of theamplifying circuit Ci connected to the input line Ai. ΔVij is apotential difference at the resistive coupling element and given by:##EQU6## Rij represents resistance of the resistive coupling element andis given by R+ or R-. Therefore, the voltage Vin (i) is a total sum ofall the outputs of the amplifying circuits connected to the data inputline Ai.

The above-mentioned operation is analogical computation. This analogicalcomputation is performed at a time in parallel in the resistive matrix100. However, both input data and output data are digital data.Subsequently, a practical computing operation will be described withreference to FIG. 4.

Input data is applied to the respective internal data input lines A1 toA4 through a register 104. The respective internal data input lines A1to A4 are charged to potential levels corresponding to the input dataand thus the neural network is initialized.

Output potentials of the amplifying circuits C1 to C4 change accordingto charging potentials applied to the data input lines A1 to A4. Thesepotential changes of the respective amplifying circuits C1 to C4, orpotential changes on the internal data output lines are fed back to theinternal data input lines A1 to A4 through the corresponding resistivecoupling elements. The potential levels, or current values fed back tothe internal data input lines A1 to A4 are defined by the programmedstates of the respective resistive coupling elements. More specifically,when a resistive coupling element has been programmed to be in the"excitatory state", current flows from the supply potential V_(DD) to adata input line Ai. On the other hand, when the resistive couplingelement has been programmed to be in the "inhibitory state", currentflows from the supply potential V_(SS) to the data input line Ai. Suchoperations proceed in parallel except for those resistive couplingelements that have been set in the open state. Thus, currents flowinginto the data input line Ai are analogically added together, causing apotential change on the data input line Ai. When the changed potentialon the data input line Ai goes over a threshold voltage of thecorresponding amplifying circuit Ci, output potential of this amplifyingcircuit Ci changes. By repeating such operation, output potential ofeach amplifying circuit Ci changes to meet the above-mentioned conditionthat the total sum of currents becomes 0, until the network settles in astate satisfying the above-described expression of the stable state.

After this neural network has been stabilized, the output voltage of theamplifying circuit Ci is stored in a register (the input/output register104 shown in FIG. 3) and then read out. A determination as to whetherthe network has been stabilized or not is made, depending on whether ornot a predetermined time has passed since the data input or by directlycomparing succeeding output data stored in the output register anddetecting difference therebetween in terms of time. In the latter case,it is determined that the network has been stabilized when differencesbetween the compared output data get smaller than a predetermined value,and then output data is provided.

This neural network outputs such output data as allowing energy of theneural network to settle in minimum values (or local minima). Thus,according to the programmed states of the resistive coupling elements,the resistive matrix 100 stores some patterns or data and can determinematch/mismatch between input data and the stored pattern or data.Therefore, such a neural network can also serve as an associative memorycircuit or a pattern discriminator.

A structure obtained by removing the feedback paths between the internaldata output lines Bi and Bi and the internal data input lines Aj in theresistive matrix 100 shown in FIG. 4 has been known as a perceptroncircuit of a single layer. This perceptron circuit can have a simplifiedlearning algorithm, and when multi-layered, a flexible system can beimplemented.

FIG. 6 shows a specific example of a possible structure of the couplingelement shown in FIG. 5. In FIG. 6, there is shown structure of acoupling element Tij disposed at a location of i row and j column in theresistive matrix, or at the connection of an internal data input line Aiand an internal data output line Bj, and the parts equivalent orcorresponding to those in the conceptual structure of the couplingelement shown in FIG. 5 are denoted by the same reference numerals.

In FIG. 6, each of the switching elements S1 to S4 is constituted of aninsulated gate field effect transistor (MIS transistor). The internaldata input line is formed of complementary signal lines Ai and Ai. Datacomplementary to each other are transmitted from the input register (seeFIG. 3) onto these paired complementary signal lines.

The bit line BL is constituted of a complementary bit line pair of BLjand BLj receiving complementary data.

The random access memory cell (RAM1) 150 comprises two invertingamplifiers IN1 and IN2 for storing coupling strength information thatare anti-parallel to each other, or cross-coupled with the input portionof one amplifier connected to the output portion of the other, and MIStransistors TR1 and TR2 responsive to a signal potential on the wordline WLiP for being turned on to connect the input portions of theinverting amplifiers IN1 and IN2 to the bit lines BLj and BLj,respectively. The inverting amplifiers IN1 and IN2 constitute a latchcircuit which stores coupling strength information. The informationlatched by the latch circuit is transmitted through a node N1 to thecontrol electrode (gate electrode) of the switching element (MIStransistor) S2.

Likewise, the random access memory cell (RAM2) 151 comprises invertingamplifiers IN3 and IN4 constituting a latch circuit, and MIS transistorsTR3 and TR4 responsive to a signal potential on the word line WLiQ forbeing turned on to connect the input portions of the invertingamplifiers IN3 and IN4 to the bit lines BLj and BLj, respectively. Theinformation stored in the latch circuit constituted of the invertingamplifiers IN3 and IN4 is applied through a node N2 to the controlelectrode (gate electrode) of the switching element(MIS transistor) S3.In the following, operation of writing the coupling strength informationinto this coupling element Tij will be briefly described.

When the word line WLiP is selected by the row decoder 102 (see FIG. 3),the MIS transistors TR1 and TR2 are turned on together, connecting theinput portions of the inverting amplifier IN1 and IN2 to the bit linesBLj and BLj, respectively. Subsequently, data of "0" and "1" aretransmitted onto the bit line BLj and its complementary bit line BLj,respectively. Then, due to the latching function of the cross-coupled oranti-parallel inverting amplifiers IN1 and IN2, data of "1" is stored atthe node N1. Thus, the "excitatory state" of the coupling element Tij isprogrammed.

When this coupling element Tij is to be programmed in the "inhibitorystate", the word line WLiQ is selected and data of "0" and "1" aretransmitted onto the bit line BLj and the complementary bit line BLj,respectively. Due to the latching function of the cross-coupledinverting amplifiers IN3 and IN4, data of "1" is latched at the node N2.When the coupling element Tij is to be programmed to take the "openstate", the word lines WLiP and WLiQ are sequentially selected and dataof "1" and "0" are transmitted onto the bit line BLj and thecomplementary bit line BLj, respectively. As a result, data of "0" arelatched at the nodes N1 and N2. Meanwhile, information of "1" representsan "H"-level signal and information of "0" represents an "L"-levelsignal.

While the coupling element shown in FIG. 6 is configured such thatsignal potentials on the internal data input lines are transmitted tothe internal data output lines, the structure becomes equivalent to thatof the coupling element shown in FIG. 5 if it is adapted such thatsignals on the internal data output lines Bj are fed back to theinternal data input lines Ai (Ai).

FIG. 7 shows the entire structure of a neural network obtained byarranging the coupling elements as shown in FIG. 6 in a matrix of 4 rowsand 4 columns.

In FIG. 7, in order to write coupling strength information into thecoupling elements, there are provided a RAM I/O 106b serving asinterface for transmitting and receiving data to and from outside of thedevice, selective gates 111 responsive to a column select signal (columndecode signal) from the bit decoder 103 for connecting the selectedcolumn to internal data buses I/O and I/O, data registers 104 providedcorresponding to the respective columns for amplifying and latching theapplied data, and transfer gates 112 responsive to an operation modeindicating signal MUX for connecting the data registers 104 to theresistive matrix 100.

A column select line is formed of a complementary bit line pair of BLand BL and, therefore, the selective gates 111 and the transfer gates112 comprise one pair of MIS transistors for each column. Among the MIStransistor pairs contained in the selective gates 111, one pair of MIStransistors are rendered conductive in response to the column selectsignal from the bit recorder 103. The transfer gates 112 are renderedconductive in a program mode where information of coupling strength iswritten in the respective coupling elements of the resistive matrix 100,and turned off in a practical processing operation where input data tobe processed by the neural network is externally applied.

The RAM I/O 106b transmits a complementary data pair to the internaldata buses I/O and I/O.

Word lines WL1P to WL4P and WL1Q to WL4Q are connected to the outputportion of the row decoder 102 to receive a row select signal from therow decoder 102.

An input register 106a, which corresponds to the input/output registerin FIG. 3 or the data input portion of an interface, has its outputportion connected to complementary internal input data line pairs of A1and A1 to A4 and A4 for transmitting complementary input signals (inputdata signals to be processed).

Amplifying circuits 101 are provided corresponding to the respectiveinternal data output lines B1 to B4 to amplify signal potentialsthereon.

In writing the information of coupling strength, the signal MUX attainsthe "H" level indicative of the active state so that the transfer gates112 are rendered conductive. Subsequently, the row decoder 102 and thebit decoder 103 select one row and one column, respectively, and thendesired information is written in the storage elements of the couplingelement located at the connection of the selected row and column. Atthis time, the data register 104 latches the complementary datatransmitted to the bit lines BL and BL and at the same time, the dataare written in the coupling elements. Though each storage element has alatch circuit constituted of cross-coupled inverters, the data register104 has a larger driving capability than the latching capability of thestorage elements. Therefore, desired coupling strength information iswritten in the respective coupling elements.

In operation, the signal MUX falls to the "L" level indicative of the"inactive state", the transfer gates 112 are turned off. Subsequently, aneuron input signal, which has been applied from outside of the chip andtemporarily stored in the input register 104, is transmitted to theinternal data input lines Ai and Ai as an input signal to be processedin the resistive matrix 100. In the resistive matrix 100, according tothe combination of the information stored in the RAM1 and RAM2 containedin the respective coupling elements Tij, charging and dischargingoperations are performed for the internal data output lines Bi inparallel. Voltage values on the internal data output lines Bi aredetected and amplified by the amplifying circuits 101 and the resultsare developed as output signals.

In the structure above, a non-Hopfield's type neural network has beendescribed where paths for feeding back signal voltages on the internaldata signal lines to the resistive matrix are not provided. However,also a Hopfield's type neural network can be configured in the samemanner if only paths for feeding back the internal data output lines Bito the internal data input lines Ai and Ai are added to theabove-described structure.

A conventional coupling element comprises storage element portions forstoring coupling strength information and a current supplying elementportion responsive to the information stored in the storage elementportions and a signal potential on an internal data input line (orinternal data output line) for transmitting a predetermined current toan internal data output line (or internal data input line). Therefore,as the number of elements constituting a coupling element increases withits structure getting more complicated, the area occupied by thecoupling element becomes larger.

Further, since the word lines and the bit lines for writing couplingstrength information in the storage element portions and the internaldata input lines and the internal data output lines for transferringdata to be processed are provided individually, a large number of signallines are required, occupying a large area. Furthermore, since the largenumber of signal lines have to be provided in a small area, layout ofthe signal lines becomes complicated, resulting in a significantobstacle in achieving higher integration. Thus, the provision of a largenumber of signal lines, combined with complicity of the structure ofcoupling element, brings about disadvantages also in terms of productionyield.

Additionally, in the conventional semiconductor neural networks, theinput data signal continues to be applied for a significant time tosufficiently charge or discharge the internal data inputs lines to the"H" or "L" level, so that when the internal data input lines and theinternal data output lines are charged or discharged in operation,potential of each signal line may make a full-swing. As a result, theconsumption power becomes large and fast operability can not beachieved, while taking a longer processing time (or convergence time).

A structure of synapse load expressive unit is disclosed in "ANeuromorphic VLSI Learning System" by J. Alspector et al, pp. 323 to 325in "Advanced Research in VLSI, 1987" published by MIT Press, where aninput signal and an output signal of neurons are coupled togetherthrough a transistor which is turned on/off under control of a flip-flopstoring a synapse coupling strength.

SUMMARY OF THE INVENTION

An object of the present invention is to provide an improvedsemiconductor neural network from which disadvantages of theconventional semiconductor neural networks as described above have beeneliminated.

Another object of the present invention is to provide a semiconductorneural network which comprises coupling elements of a simple structure.

Still another object of the present invention is to provide asemiconductor neural network whose resistive matrix has a reduced numberof signal lines.

A further object of the present invention is to provide a semiconductorneural network operable at a high speed and with low consumption power,and a driving method thereof.

A semiconductor neural network according to the present inventioncomprises a plurality of internal data input lines, a plurality ofinternal data output lines, a plurality of word lines, bit lines sharedwith the internal data output lines, and coupling elements provided atthe connections of the bit lines and the word lines.

A coupling element comprises storage means constituted of cross-coupledor anti-parallel inverting amplifiers for storing information indicativeof a specific coupling strength, means responsive to a signal potentialon a corresponding word line for being rendered conductive to write asignal potential on a corresponding bit line (internal data output line)in the storage means, and means responsive to a signal potential on acorresponding internal data input line for being rendered conductive totransmit the information stored in the storage means onto acorresponding internal data output line (bit line).

The coupling element has first and second storage means.

The bit lines have a structure constituted of complementary bit linepairs and thus also the internal data output lines have a structureconstituted of complementary internal data output line pairs. Theinformation stored in the first storage means is transmitted onto firstinternal data output lines of the complementary internal data outputline pairs, and the information stored in the second storage means istransmitted onto second internal data output lines (second bit lines) ofthe bit line pairs (internal data output line pairs).

The semiconductor neural network according to the present inventionfurther preferably comprises means for detecting a time of change of anexternally applied input data signal, converting, in response to adetection signal, the externally applied input data signal into aone-shot pulse signal and transmitting the converted signal onto theinternal data input lines.

The semiconductor neural network according to the present inventionfurther preferably comprises means responsive to the signal detecting achange of the externally applied input data for equalizing potentials onthe internal data output lines, and means responsive to the signaldetecting a change of the input data signal for activating senseamplifiers provided to the internal data output lines.

As previously described, since the same signal lines are shared by thebit lines and the internal data output lines, a reduction in the numberof signal lines becomes possible. For the coupling elements, since theinformation stored in the storage means is transmitted to the internaldata output lines in response to signal potentials on the internal datainput lines, the number of elements constituting a coupling element canbe reduced. Thus, coupling elements occupying only a small area can beobtained.

Further, since in a coupling element, one out of the two storage meansis connected to a positive internal data output line and the otherstorage means has its storage node connected to a negative bit line(internal data output line), a device structure where commonly use ofthe bit line pairs and the internal data output line pairs is realizedcan be obtained.

Furthermore, by converting the internal input data signal into aone-shot pulse signal, the full-swing of potentials on the internalsignal lines can be prevented. As a result, low power consumption andfast operability can be achieved, while preventing inversion of thestorage information in the storage elements constituted of invertingamplifiers. Thus, stable operation of the storage elements in thecoupling elements is assured.

Furthermore, by equalizing potentials on the internal data output linepairs and activating the sense amplifiers in response to a change of theinternal input data signal, low power consumption as well as fastoperability can be achieved.

The foregoing and other objects, features, aspects and advantages of thepresent invention will become more apparent from the following detaileddescription of the present invention when taken in conjunction with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is diagram showing a neuron model.

FIGS. 2A and 2B are diagrams showing examples of threshold function of aneuron.

FIG. 3 is a diagram showing conceptual structure of a conventionalsemiconductor neural network chip.

FIG. 4 is a diagram showing conceptual structure of the major part of aconventional semiconductor neural network chip.

FIG. 5 is a diagram schematically showing structure of a basic synapsecoupling element of the neural network shown in FIG. 4.

FIG. 6 is a diagram showing a specific structure of the basic synapsecoupling element shown in FIG. 5.

FIG. 7 is a diagram showing structure of a conventional semiconductorneural network chip using the basic synapse coupling elements as shownin FIG. 6.

FIG. 8 is a diagram schematically showing the entire structure of asemiconductor neural network chip using the coupling elements as shownin FIG. 9.

FIG. 9 is a diagram showing structure of a coupling element used in asemiconductor neural network according to an embodiment of the presentinvention.

FIG. 10 is a signal waveform chart showing operation of thesemiconductor neural network shown in FIGS. 8 and 9.

FIG. 11 is a diagram showing structure of a coupling element accordingto another embodiment of the present invention.

FIG. 12 is a diagram showing structure of a coupling element accordingto still another embodiment of the present invention.

FIG. 13 is a diagram showing the entire structure of a semiconductorneural network using the coupling elements as shown in FIG. 12.

FIG. 14 is a diagram schematically showing the entire structure of asemiconductor neural network chip which has a feedback circuit using thecoupling elements as shown in FIG. 9.

FIG. 15 is a diagram schematically showing structure of a Hopefield'stype semiconductor neural network using the coupling elements as shownin FIG. 12.

FIG. 16 is a diagram showing an example of structure of an invertingamplifier circuit constituting the storage element contained in acoupling element.

FIG. 17 is a diagram showing another structure of inverting amplifiercircuit constituting the storage element contained in a couplingelement.

FIG. 18 is a diagram showing a circuit structure for generating internaloperation control signals in a semiconductor neural network, accordingto an embodiment of the present invention.

FIG. 19 is a signal waveform chart showing operation of the circuitshown in FIG. 18.

FIG. 20 is a diagram showing a circuit structure for generating otherinternal signals in a semiconductor neural network, according to thepresent invention.

FIG. 21 is a signal waveform chart showing operation of the circuitshown in FIG. 20.

DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 8 shows schematically the entire structure of a semiconductorneural network according to an embodiment of the present invention. Thestructure shown in FIG. 8 corresponds to that of the conventionalsemiconductor neural network shown in FIG. 7, and the same or equivalentparts are denoted by the same reference numerals.

In FIG. 8, the semiconductor neural network employs complementary bitline pairs of BL and BL also as internal data output line pairs. Outputsignal lines of an input register 106a are of a single line structure.Amplifying circuits 101 are provided corresponding to the bit line pairsand comprises amplifiers for differentially sensing and amplifyingpotentials on the corresponding bit line pairs.

Second transfer gates 115 are provided to connect the bit line pairs(internal data output line pairs) of BL1 and BL1 to BL4 and BL4 to theamplifying circuits 101 selectively according to operation modes. Thesecond transfer gates 115 are rendered conductive in response to aconnection control signal MUX. In operation modes such as learning mode,or self-organizing mode where coupling strengths of the respectivecoupling elements in the coupling matrix 100 are programmed, the controlsignal MUX falls to "L" to disconnect the coupling matrix 100 from theamplifying circuits 101. On the other hand, in operation modes such asrecalling mode where operational processings are performed on externallyapplied input data signals, the control signal MUX attains the "H" levelto connect the coupling matrix 100 to the amplifying circuits 101. Thatis, the first and second transfer gates 112 and 115 performcomplementary operations to each other.

FIG. 9 shows a specific structure of the coupling element Tij shown inFIG. 8. In FIG. 9, the coupling element Tij comprises invertingamplifiers IN10 and IN11 for storing information for a complementary bitline BLj, and inverting amplifiers IN12 and IN13 for storing informationfor a bit line BL. The inverting amplifiers IN10 and IN11 are arrangedanti-parallel with each other, or cross-coupled to each other toconstitute a latch circuit. Likewise, the inverting amplifiers IN12 andIN13 are arranged anti-parallel with each other, or cross-coupled toeach other to constitute another latch circuit.

The coupling element Tij further comprises a switching transistor S5responsive to a signal potential on the word line WLiP for being turnedon to connect a node N3 to the bit line BLj, a switching transistor S6responsive to a signal potential on the word line WLiP for being turnedon to connect another node N1 to the complementary bit line BLj, and aswitching transistor S7 responsive to a signal potential on the internaldata input line Ai for being turned on to connect the node N1 to thecomplementary bit line BLj. The coupling strength information stored inthe latch circuit constituted of the inverting amplifiers IN10 and IN11is transmitted to the complementary bit line BLj in response to thesignal potential on the data input line Ai.

The coupling element Tij further comprises switching transistors S9 andS10 responsive to a signal potential on the word line WLiQ to connectnodes N2 and N4 to the bit line BLj and the complementary bit line BLj,respectively, and a switching transistor S8 responsive to the signalpotential on the internal data input line Ai to connect the node N2 tothe bit line BLj. The coupling strength information stored in thestorage circuit portion constituted of the inventing amplifiers IN12 andIN13 is transmitted onto the bit line BLj in response to the signalpotential on the internal data input line Ai.

The coupling state of the coupling element Tij is determined accordingto the combination of signal potentials stored at the storage nodes N1and N2. When signal potentials of the "L" and "H" levels have beenstored at the nodes N1 and N2, respectively, the coupling element Tijindicates the "excitatory state". When potentials of the same level havebeen stored at the storage nodes N1 and N2, no potential differenceappears on the bit line pair of BLj and BLj so that the coupling elementTij indicates the "don't care (open) state". When signal potentials ofthe "H" and "L" levels have been stored at the nodes N1 and N2,respectively, the coupling element Tij has been programmed to indicatethe "inhibitory state".

In the following, operation of the neural network shown in FIGS. 8 and 9will be described with reference to the operation waveform chart of FIG.10.

When the control signal MUX is set to the "H" level and thecomplementary control signal MUX is set to the "L" level, thesemiconductor neural network is put in an operation mode where couplingstrength data are written in or read out of the respective couplingelements contained in the coupling matrix 100. The control signal MUXdetermines a period for which storage contents in the storage elementsare rewritten to change weighting of the synapse couplings in theself-organizing process, or learning.

When the control signal MUX attains "H", the first transfer gates 112are rendered conductive so that the data register 104 is connected tothe coupling matrix 100.

Subsequently, a row address and a column address are externally appliedto the row decoder 102 and the bit decoder 103, respectively, to selecta corresponding word line (WLiP or WLiQ) and a corresponding bit linepair of BLj and BLj. Meanwhile, the RAM I/O 106b converts externallyapplied information of coupling strength Din into a complementary datapair and transmits the converted data to the internal data buses I/O andI/O. The complementary data pair transmitted to the internal data busesI/O and I/O is transmitted through a selective gate (one pair oftransistors contained in the gates 111) selected by the bit decoder 103to a corresponding data register to be latched therein.

The switching transistors S5 and S6 associated with the selected wordline (for example, WLiP) are turned on connecting the latch circuitsconstituted of the inverting amplifier circuits to the bit line pairs ofBLj and BLj. As a result, the data latched in the data register istransmitted through the switching transistors S5 and S6 to the storagenodes N1 and N3 to be latched therein. Meanwhile, the driving capabilityof the data register 104 is of course larger than the latchingcapability of the inverting amplifier circuit of IN10 and IN11 (IN12 andIN13). Therefore, it is possible to write signal potentials indicativeof desired coupling strength information into the storage nodes N1 andN3.

When the word line WLiQ has been selected, information indicative of adesired coupling strength is written in the latch circuit constituted ofthe inverting amplifiers IN12 and IN13 so that signal potentialscorresponding to the coupling strength information are stored in thenodes N2 and N4, respectively. By performing the above-describedoperation for the respective storage elements of the coupling elementsT11 to T44 in the coupling matrix 100, information of coupling strengthscan be programmed in the coupling matrix 100. This writing of thecoupling strength information into the coupling matrix 100 is similar tothe data writing operation in a general static random access memory.

Subsequently, when the control signal MUX falls to the "L" level and thecomplementary control signal MUX attains the "H" level, the firsttransfer gates 112 are put in the non-conductive state and the secondtransfer gates 115 are rendered conductive, putting the neural networkin an operation mode specific to the neural network. An external datasignal applied from outside of the neural network chip 200 is first heldby the input register 106a and then the thus held external data signalis transmitted from the input register 106a to the internal data signallines A1 to A4 as an internal data signal. In this operation mode, allthe output potentials of the row decoder 102 are fixed at the "L" levelso that all the switching transistors S5, S6, S9 and S10 (see FIG. 9)contained in the coupling elements are in the off-state.

Now, when the signal potential on the internal data input line Ai risesto the "H" level in response to the internal data signal, the switchingtransistors S7 and S8 of the coupling element Tij are turned on so thatthe signal potentials stored in the coupling element Tij are transmittedto the bit line pair (internal data output line pair) of BLj and BLj.When the coupling element Tij has been programmed to be in the"excitatory state", the bit line BLj receives a signal potential of the"H" level and the complementary bit line BLj receives a signal potentialof the "L" level. When the coupling element Tij has been programmed tobe in the "inhibitory state", the bit lines BLj and BLj receive signalpotentials of the "L" and "H" level, respectively. When the couplingelement has been programmed to be in the "don't care (open) state", thebit lines BLj and BLj receive potentials of the same level.

When the signal potential on the internal data signal line Ai is at the"L" level, the switching transistors S7 and S8 are in the off state.Thus, the coupling element is in a state equivalent to the "don't care(open) state" since no potential change occurs on the bit line pair BLjand BLj, irrespective of the storage information of the coupling elementTij. Such changes of signal potentials on the bit line pairs (internaldata output line pairs), which occur according to the signal potentialson the internal data signal lines, proceeds at a time and in parallel inthe respective coupling elements of the matrix 100. The signalpotentials appearing on the bit line pairs of BL1 and BL1 to BL4 and BL4as a result of the parallel operation are sensed and amplified by theamplifying circuits 101 and then converted into corresponding datasignals to be output as neuron output data.

In the structure of coupling element shown in FIG. 9, a word line, a bitline and an internal data input line are each connected to the samenumber of transistors and have the same stray capacitance associatedtherewith. However, the number of transistors connected to the inputportion of one inverting amplifier circuit is different from the numberof transistors connected to the output portion of the same circuit. Thisleads to an imbalanced stray capacitance in one storage element portionand also to imbalanced latching capability of the latch circuit. As aresult, the writing or reading operation of the coupling strengthinformation becomes unstable, and in some cases, the coupling strengthinformation may not be precisely stored and read out.

In FIG. 11, there is shown a structure of coupling element which canavoid such an unbalanced stray capacitance in the storage elementportion and stabilize the operations of reading and writing the couplingstrength information in the storage element portion. In the structure ofcoupling element shown in FIG. 11, dummy capacitors C1 and C2 areconnected between the nodes N3 and N4 and the internal data input lineAi, respectively. The dummy capacitors C1 and C2 are formed byconnecting conductive terminals of MIS transistors and equalize straycapacitances in the inverting amplifier circuits of IN10 and IN11, andof IN12 and IN13. In this case, the switching transistors S5, S6, S7,S8, S9 and S10 and the dummy capacitators C1 and C2 are constituted ofMIS transistors of the same size. With this structure, the straycapacitances in the storage element portions can be easily balanced,making the reading and writing operations of the coupling strengthinformation stable.

FIG. 12 shows a second modified structure of the coupling element shownin FIG. 9. The coupling element comprises a switching transistor S5responsive to a signal potential on a first word line WLiP (1) for beingturned on to connect the node N3 to the bit line BLj, a switchingtransistor S6 responsive to a signal potential on a second word lineWLiP (2) for being turned on to connect the node N1 to the complementarybit line BLj, and inverting amplifiers IN10 and IN11 cross-coupled toeach other between the nodes N1 and N3. The coupling element furthercomprises a switching transistor S9 responsive to a signal potential ona third word line WLiQ(1) for being turned on to connect the node N2 tothe bit line BLj, a switching transistor S10 responsive to a signalpotential on a fourth word line WLiQ(2) to connect the node N4 to thecomplementary bit line BLj, and inverting amplifier IN12 and IN13cross-coupled to each other between the nodes N12 and N4.

The first and second word lines WLiP(1) and WLiP(2) receive the same rowselect signal. The third and fourth word lines WLiQ(1) and WLiQ(2)receive the same row select signal.

Further, the word lines WLiP(1) and WLiQ(1) are used also as an internaldata input line Ai to receive the same internal data signal.

In the basic element structure shown in FIG. 12, the stray capacitancesin the storage element portions can be balanced without providing thedummy capacitors to balance them. Furthermore, since the number oftransistors constituting a basic coupling element is less, the areaoccupied by the coupling element can be reduced.

Additionally, in the basic coupling element structures shown in FIGS. 9,11 and 12, the double-end structure is employed so that the couplingstrength information is written in through both bit line BLj and itscomplementary bit line BLj. Instead of such structure, a single-endstructure may be employed which is obtained, for example, by eliminatingthe switching transistors S6 and S9 from the coupling element structureshown in FIG. 9 so that information of a desired coupling strength bewritten in through only one of the paired bit lines.

FIG. 13 shows the entire structure of a semiconductor neuron networkwith coupling matrix 100 having coupling elements as shown in FIG. 12arranged in 4 rows and 4 columns. In the structure shown in FIG. 13, theword lines and the internal data input lines share the same signallines. Therefore, in addition to the structure shown in FIG. 8, thereare provided connection gates 116 responsive to control signal MUX forbeing turned on to connect row decoder 102 to the coupling matrix 100and connection gates 117 responsive to complementary control signal MUXto connect neuron input register 106a to the coupling matrix 100.Further, since the internal data input lines are required to correspondto the respective two storage element circuit portions of the couplingelements, two signal lines transmitting the same internal data inputsignal are provided as internal data input lines for a single couplingelement. Furthermore, a single coupling element requires four wordlines, the output signal lines of the row decoder 102, or word lines aredoubled in number when compared with the structure shown in FIG. 8. Forthe output signal lines, or the word lines of the row decoder 102 shownin FIG. 13, word lines denoted by the same reference numeral receive thesame row select signal.

In such a structure as shown in FIG. 13 where either the neuron inputregister 106a or the row decoder 102 is connected to the coupling matrix100 according to operation modes, the word lines and the internal datainput lines can share the same signal lines, as previously described.Thus, in practice, a doubled number of signal lines are not required,but the number of signal lines increases by only one for one row, sothat a coupling matrix occupying a small area can be realized.

Meanwhile, the semiconductor neural networks shown in FIGS. 8 and 13have non-Hopfield's type structures where paths for feeding back theinternal output data signals into the coupling matrix are not provided.However, these structures can be easily modified to constituteHopfield's type neural networks only with the provision of the feedbackpaths. FIG. 14 shows an example of structure of a Hopfield's typesemiconductor neural network using coupling elements according to thepresent invention.

In FIG. 14, the Hopfield's type semiconductor neural network accordingto the present invention comprises inverting amplifiers 120a, 120b, 120cand 120d for inverting outputs of complementary bit lines BL1, BL2, BL3and BL4 and transmitting the inverted outputs to internal data inputlines A1, A2, A3 and A4. The inverting amplifiers 120a and 120d allowthe internal data output signals from the coupling matrix 100 to be fedback through the internal data input signal lines. Thus, a neuralnetwork according to the Hopfield's model is configured.

FIG. 15 shows an example of structure of a Hopfield's type semiconductorneural network obtained by modifying the non-Hopfield's typesemiconductor neural network shown in FIG. 13. In the semiconductorneural network shown in FIG. 15, feedback amplifying circuits 121 areprovided to transmit signal potentials on the respective bit line pairsof BL1 and BL1 to BL4 and BL4 to the internal data input line pairs A1to A4. Each of the feedback amplifying circuits 121 comprises aninverting amplifier 20a for inverting potential on a complementary bitline BLi and transmitting the inverted potential onto a correspondinginternal data input line Ai, and inverting amplifiers 20b and 20cconnected in series over two stages for transmitting potential on apositive bit line BLi onto another corresponding input data signal lineAi. In the structure shown in FIG. 15, the feedback paths are providedin the same manner though both the internal data input lines and theinternal data output lines form pairs, so that a Hopfield's type neuralnetwork is configured.

FIG. 16 shows an example of structure of an inverting amplifiercomprised in the storage element portion of a coupling element. In FIG.16, an inverting amplifier circuit is formed of a complementary (CMOS)logic circuit comprising a p-channel MIS transistor PMOS and ann-channel MIS transistor NMOS. The structure shown in FIG. 16 ischaracterized by its large output driving capability and large noisemargin. These characteristics allow the circuit to transmit precisecoupling strength information onto a corresponding bit line in areliable manner.

FIG. 17 shows another structure of an inverting amplifier comprised inthe storage element portion of a coupling element. In the structureshown in FIG. 17, an inverting amplifier circuit is constituted of aload resistor R1 formed of, for example, polysilicon and an n-channelMIS transistor NMOS. The resistor R1 transmits a predetermined supplypotential Vcc to the output portion. The n-channel MIS transistor NMOSreceives an input signal IN at its gate and drives the output portionaccording to its gate potential. Since in the structure of invertingamplifier shown in FIG. 17, the load resistor is employed, the circuitsize can be made smaller than that of the inverting amplifier shown inFIG. 16.

While in all the embodiments described above, it has been simply statedthat in the operational processings of the neural network, the dataregister 104 is disconnected from the coupling matrix 104 and instead,the amplifying circuits 101 are connected to the coupling matrix 100, noreference has been made to initialization of the bit line potentials. Ininitializing the bit lines that are to be held in the floating state, ittakes a long time in the operational processings to establish potentialson the bit lines that have been held at the levels transmitted inprogramming the coupling strengths of the respective coupling elementsin the coupling matrix 100. On the other hand, if all the bit lines areset either to the "L" or "H" level in the initialization, it takes along time to establish the "H" or "L" level on the bit lines. This leadsto longer charging and discharging time, resulting in an increased powerconsumption. To overcome such disadvantages, a circuit structure whichcan drive the bit lines reliably at a high speed and with low powerconsumption is shown in FIG. 18.

Referring to FIG. 18, the bit line driving circuit comprises a signalchange detecting circuit 701 for detecting a time of change of anexternal or internal input data signal Ai and generating a one-shotpulse signal (input change detecting signal) ATD having a predeterminedtime interval, a BLEQ generating circuit 702 responsive to the inputchange detecting signal ATD for generating an equalize signal BLEQhaving a predetermined time interval, and an SAE generating circuit 703responsive to the input change detecting signal ATD and the equalizesignal BLEQ for generating a sense amplifier activating signal SAE. Theequalize signal BLEQ from the BLEQ generating circuit 702 is transmittedto equalize transistors EQT provided to short-circuit paired bit lines.

The signal change detecting circuit 701 has the same structure as thatof a circuit for generating an address change detecting signal whichgenerates internal operation signals, for example, in a random accessmemory. The generation of the input change detecting signal ATD allows astarting point of an computing operation cycle to be reliably detected.Since it is desirable that operation timings of the circuit can bedetected as early as possible, an externally applied input data signalmay be preferably supplied as the input data signal Ai to the signalchange detecting circuit 701. In the following, operation of the circuitshown in FIG. 18 will be described with reference to the operationwaveform chart of FIG. 19.

When the neural network enters the operational processing mode, orrecalling operation mode, the control signal MUX falls to "L" and thecomplementary control signal MUX attains the "H" level. As a result, thetransfer gates 115 are rendered conductive, connecting the bit lines BLjand BLj to the amplifying circuits 101. Subsequently, the input signalAi is applied and then a point of change of the input signal Ai isdetected by the signal change detecting circuit 701 to generate theinput change detecting signal ATD. This input change detecting signalATD has a predetermined time interval. In response to the input changedetecting signal ATD, the BLEQ generating circuit 702 is activated togenerate the equalize signal BLEQ. In response to this equalize signalBLEQ, the equalize transistors EQT are rendered conductive so thatpotentials on the paired bit lines BLj and BLj are equalized. Meanwhile,the signal waveform chart shown in FIG. 19 shows a case where the bitline pairs of BLj and BLj are equalized at an intermediate potentialbetween the "H" and "L" levels. This can be realized by providing therespective bit lines with transistors which are rendered conductive, inresponse to the equalize signal BLEQ, to precharge the bit lines to theintermediate potential. The equalize signal BLEQ is a one-shot pulsesignal having a predetermined time interval. Therefore, when thegeneration of the pulse signal terminates, subtle potential differencesappear over the bit lines BLj and BLj due to functions of the couplingelements.

Subsequently, when the generation of the input change detecting signalATD and the equalize signal BLEQ terminates, the SAE generating circuit703 is activated to generate the sense amplifier activating signal SAE.Then, the amplifying circuits 101 are activated to differentially senseand amplify the subtle differences of signal potentials on thecorresponding bit line pairs of BLj and BLj and output the results asneuron output signals. Meanwhile, in the operation waveform chart shownin FIG. 19, a case is shown where the signal potentials of the bit linepairs of BLj and BLj make full-swing between the "H" and "L" levels dueto functions of the sense amplifier circuits 101. However, thedifferences of signal potentials can be sensed and amplified for outputwithout the potentials on the bit lines being affected by suchfull-swing if only current-mirror type differential amplifier circuitsare employed, for example.

As described above, by equalizing potentials on the bit line pairsimmediately after the neuron input data signal has changed, potentialschanges which might be caused under the influences of noise and the likeon the bit line pairs can be prevented. Accordingly, it becomes possibleto shorten the time taken until effective information appears on the bitlines, so that a neural network operable at a high speed and with lowpower consumption can be obtained.

If the equalize transistors EQT for equalizing the bit line pairs havebeen adapted to be able to function also in programming couplingstrengths of the coupling elements in the coupling matrix 100, theprogramming of coupling states of the coupling matrix can be performedalso at a high speed. Additionally, a circuit structure for prechargingthe bit line pairs to the predetermined potential may be separatelyprovided.

Further, if the equalize/precharge transistors are provided as describedabove, subtle differences of the potentials can be differentially sensedand amplified in a reliable manner. Accordingly, activation timings ofthe amplifying circuits can be made earlier, so that a neural networkoperable at a high speed can be obtained.

When a large scale parallel operation is performed in the couplingmatrix corresponding to the internal input data signal applied for along time, potentials on the respective bit line pairs may havefull-swing. In this case, since those coupling elements whose internalinput data signals are at the "H" level have the storage nodes of theirstorage elements connected to the bit lines, the contents stored in thestorage elements may be rewritten by the potentials on the bit lines dueto the full-swing, and further, the storage contents of the storageelements may be even destructed. A circuit structure for stabilizing thestorage information in the coupling elements is shown in FIG. 20.

Referring to FIG. 20, the circuit structure for stabilizing the storageinformation in the coupling elements comprises signal change detectingcircuit 710 for detecting a time of change of an externally appliedinput data signal Ex. Ai and generating, in response to the detectedchange, input change detecting signal ATD of one-shot pulse having apredetermined time interval, and gate circuits 711 and 712 which receivethe input change detecting signal ACD and the externally applied inputdata signal Ex. Ai for generating one-shot internal input data signalsInt. Ai. The gate circuits 711, 712 and the like are providedcorresponding to the respective bits of the external input data signal.An operation waveform chart of the circuit structure shown in FIG. 20 isshown in FIG. 21. When the internal input data signal Ai is formed as aone-shot pulse signal using such a circuit structure as shown in FIG.20, the time taken to perform operational processings on the internalinput data signal becomes short, no longer allowing the full-swing ofsignal potentials on the bit line pairs of BLj and BLj even if a largescale parallel operation is performed in the coupling matrix. That is,since the operation time is short, only small potential changes occurwhich appear usually before full-swing. Thus, the destruction of thestorage information in the coupling elements can be prevented. Further,when the input data signal is converted into a one-shot pulse signalaccording to the input change detecting signal ACD, the switchingtransistors in the coupling elements are turned off at earlier timings.This means that the coupling elements are disconnected from the bit linepairs at earlier timings, preventing the destruction of the storagecontents in the coupling elements.

In the signal waveform chart shown in FIG. 21, the signal potentials onthe bit lines BLj and BLj are equalized every time the input changedetecting signal is generated, because the equalize signal as shown inFIG. 18 is generated. When such a one-shot internal input data signal asshown in FIG. 21 is employed, the amplifying circuits can differentiallysense and amplify signal potentials on the bit line pairs in a reliablemanner, no matter how small the potential changes on the bit lines BLjand BLj may be. Accordingly, operation performance of the neural networkcan not be reduced.

When the circuit structure shown in FIG. 20 is employed, the inputchange detecting signal ACD shown in FIG. 20 is applied also to BLEQgenerating circuit 702 generating bit line equalize signal BLEQ and toSAE generating circuit 703 generating activation signal SAE to activateamplifying circuits.

In the embodiments above, a circuit structure for programming couplingstrengths in the coupling matrix has been described as allowing writingof the coupling strength information for one column after another, orbit by bit, with the use of bit line decoder 103. Instead of suchstructure, however, shift registers may be provided over a number ofstages corresponding to the number of coupling elements in one row. Inthis case, coupling strength information of one row is written in theshift registers and then the coupling strength information istransferred from the shift registers to one row of coupling elements ata time.

As has been described above, according the present invention, each ofthe coupling elements contained in the coupling matrix of a neuralnetwork is constituted of storage element portions comprisingcross-coupled inverting amplifiers, and elements responsive topotentials on word lines for writing signal potentials on bit lines intothe storage elements and also responsive to potentials on internal inputdata signal lines for transmitting the storage information in thestorage element portions onto the bit line. Further, the signal linesfor transmitting coupling strength information in the learning modewhere the coupling strength information is to be written and the datalines for outputting the internal data output signals in the neuralnetwork operating mode, or recalling mode share the same lines.Accordingly, the number of interconnections in the coupling matrix andalso the number of elements constituting a coupling element can bereduced, so that coupling elements which have a simple structure andoccupy a small area can be provided. Thus, a highly integrated andhigh-density semiconductor neural network can be obtained.

Furthermore, since the internal input data signal is formed as aone-shot pulse signal to be transmitted into the coupling matrix, thebit line potentials do not make full-swing and the coupling elements aredisconnected from the bit lines, or internal data lines at earliertimings. Accordingly, the neural network can be driven at a high speedand with low consumption power and stable operation can be assuredwithout causing destruction of the storage information in the couplingelement.

Furthermore, since potentials on the bit lines are equalized in responseto the input data signal change detecting signal, it becomes possible tosense and amplify the potentials on the bit lines at a high speedwithout being influenced by noise and the like. Consequently, asemiconductor neural network operable at a high speed and with lowconsumption power can be obtained.

Although the present invention has been described and illustrated indetail, it is clearly understood that the same is by way of illustrationand example only and is not to be taken by way of limitation, the spiritand scope of the present invention being limited only by the terms ofthe appended claims.

What is claimed is:
 1. A coupling element provided at the crossing of aninternal data input line and an internal data output line in a neuralnetwork, comprising:storage means constituted of an inverter latch forstoring information corresponding to a specific coupling strength;writing means responsive to a row select signal for being turned on towrite a signal potential on the internal data output line to saidstorage means; and storage information transmitting means responsive toa signal potential on the internal data input line for being turned onto transmit the information stored in said storage means onto saidinternal data output line, said internal data output line receivingcoupling strength information in a program mode where the couplingstrength information is written in said coupling element, and saidinternal data output line receiving a data signal associated with aninternal data signal in an operation mode for processing an input datasignal, wherein said internal data output line includes a first andsecond signal line pair for receiving related coupling strengthinformation complementary to each other in said program mode; saidstorage means comprisesa first storage means including a first couplingelement having storage information transmitting means responsive to asignal potential on the internal data input line for transmitting, insaid operating mode, stored information corresponding to said specificcoupling strength to said first signal line, and a second storage meansincluding a second coupling element having storage informationtransmitting means responsive to the signal potential on said internaldata input line for transmitting, in said operation mode, storedinformation corresponding to said specific coupling strength to saidsecond signal line, said first and second coupling elements togethertransmitting one coupling strength; said neutral network includes a wordline for transferring a coupling element select signal corresponding tosaid row select signal; said word line includes a first word line forselecting said first coupling element and a second word line forselecting said second coupling element; each of said first and secondstorage means has first and second nodes; said first coupling elementcomprisesfirst and second switching elements responsive to a signalpotential on said first word line for connecting said first and secondnodes to said second and first signal lines, respectively, and a thirdswitching element responsive to the signal potential on said internaldata input line for connecting said second node to said first signalline; and said second coupling element comprisesfourth and fifthswitching elements responsive to a signal potential on said second wordline for connecting said first and second nodes to said second and firstsignal lines, respectively, and a sixth switching element responsive tothe signal potential on said internal data input line for connectingsaid first node to said first signal line, said writing means includingsaid first, second, fourth and fifth switching elements and said storageinformation transmitting means including said third and sixth switchingelements.
 2. The coupling element according to claim 1, whereinsaidfirst to sixth switching elements have capacitances associatedtherewith, said first coupling element includes a first capacitorelement means provided between said first node and said associatedinternal data input line and having the same capacitance value as thatof said third switching element, and said second coupling elementincludes a second capacitor element means provided between said secondnode and said associated internal data input line and having the samecapacitance value as that of said sixth switching element.
 3. Thecoupling element according to claim 2, whereineach of said first tosixth switching elements is constituted of a first insulated gate fieldeffect transistor, said first capacitor element means is constituted ofa second insulated gate field effect transistor having its gateconnected to said internal data input line and both of its conductiveterminals connected to said first node and having the same size as thatof said first insulated gate field effect transistor, and said secondcapacitor element means is constituted of a third insulated gate fieldeffect transistor having its gate connected to said associated internaldata input line and both of its conductive terminals connected to saidsecond node and having the same size as that of said first insulatedgate field effect transistor.
 4. A coupling element provided at thecrossing of an internal data input line and an internal data output linein a neural network, comprising:storage means constituted of an inverterlatch for storing information corresponding to a specific couplingstrength; writing means responsive to a row select signal for beingturned on to write a signal potential on the internal data output lineto said storage means; and storage information transmitting meansresponsive to a signal potential on the internal data input line forbeing turned on to transmit the information stored in said storage meansonto said internal data output line, said internal data output linereceiving coupling strength information in a program mode where thecoupling strength information is written in said coupling element, andsaid internal data output line receiving a data signal associated withan internal data signal in an operation mode for processing an inputdata signal, wherein, said internal data output line includes a firstand second signal line pair for receiving related coupling strengthinformation complementary to each other in said program mode; saidstorage means comprisesa first storage means including a first couplingelement having storage information transmitting means responsive to asignal potential on the internal data input line for transmitting, insaid operating mode, stored information corresponding to said specificcoupling strength to said first signal line, and a second storage meansincluding a second coupling element having storage informationtransmitting means responsive to the signal potential on said internaldata input line for transmitting, in said operation mode, storedinformation corresponding to said specific coupling strength to saidsecond signal line, said first and second coupling elements togethertransmitting one coupling strength; said neural network includes a wordline for transferring a coupling element select signal corresponding tosaid row select signal; said word line includes a first word line forselecting said first coupling element and a second word line forselecting said second coupling element; said first word line includesfirst and second sub-word lines for receiving the same coupling elementselect signal, and said second word line including third and fourthsub-word lines for receiving the same coupling element select signal;said internal data input line includes first and second data input linesfor receiving the same input data signal for processing, said first datainput line and said second sub-word line sharing the same signal line,and said second data input line and said third sub row select linesharing the same signal line; each of said first and second storagemeans has first and second nodes; said first coupling element comprisesafirst switching element responsive to a signal potential on said firstsub-word line for connecting one of said first and second nodes to saidsecond signal line, and a second switching element responsive to asignal potential on said second sub-word line for connecting the otherof said first and second nodes to said first signal line; said secondcoupling element comprises a third switching element responsive to asignal potential on said third sub-word line for connecting one of saidfirst and second nodes to said second signal line, and a fourthswitching element responsive to a signal potential on said fourthsub-word line for connecting the other of said first and second nodes tosaid first signal line, said writing means including said first andthird switching elements and said storage information transmitting meansincluding said second and fourth switching elements, and said first andsecond sub-word lines receive simultaneously a coupling element selectsignal for selecting said first coupling element, said third and fourthsub row select lines receives simultaneously a coupling element selectsignal for selecting said second coupling element in said program mode,and said second and third sub row select lines receives the same inputdata signal to be processed in said operation mode.